Adaptive Potential Field with Collision Avoidance for Connected Autonomous Vehicles

Abstract

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-tovehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

Collision Risk in Autonomous Vehicles: Classification, Challenges, and Open Research Areas

“…Ref. [25] introduces TTCA-LC for safer lane changes, ref. [27] emphasizes diverse behaviors at intersections, and [28] showcases an ensemble model for accuracy using driver, roadway, and weather data.…”

“…In [23,24], the authors used signal processing techniques for TTC estimation. In [25,26], the authors used potential field and cubic polynomials and an open-source software called CARLA for time-to-collision estimation.…”

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“…However, usually, reinforcement learning techniques suffer from distributional shifts or gaps between the simulation environment and the real world [12]. Other approaches such as rule-based methods [13], [14] cannot fully handle edge cases in every scenario and situation. Moreover, they are computationally heavy to implement.…”